SCH: AI-Enhanced Multimodal Sensor-on-a-chip for Alzheimer's Disease Detection

SCH：用于阿尔茨海默病检测的人工智能增强型多模态芯片传感器

基本信息

批准号：
10685378
负责人：
Juejun Hu
金额：
$ 29.77万
依托单位：
PENNSYLVANIA STATE UNIVERSITY, THE
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10685378
关键词：
Academia Address Affect Algorithmic Software Alzheimer disease detection Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer’s disease biomarker Amyloid Proteins Artificial Intelligence Binding Biological Markers Biosensing Techniques Biosensor Blood Body Fluids Cessation of life Collaborations Data Data Scientist Dementia Detection Devices Drug Industry Elderly Enzyme-Linked Immunosorbent Assay Feedback Fiber Optics General Hospitals Goals Health Human Immunohistochemistry Impaired cognition Interdisciplinary Study Knowledge Learning Machine Learning Magnetic Resonance Imaging Mass Spectrum Analysis Massachusetts Measures Mechanics Memory Methods Mining Mission Modality Modeling Nanotechnology Neurodegenerative Disorders Optics Output Patients Performance Personal Satisfaction Personality Positioning Attribute Raman Spectrum Analysis Research Research Personnel Saliva Scientist Sensitivity and Specificity Signal Transduction Software Tools Somatotype Source Code Statistical Data Interpretation System Techniques Training Weight Western Blotting Work analytical tool apolipoprotein E-4 artificial intelligence algorithm biomarker discovery biomarker identification cantilever cost data repository deep learning algorithm design design verification detection method detection platform effectiveness evaluation flexibility health care service organization heterogenous data improved innovation insight machine learning framework machine learning method medical schools minimally invasive multimodality nanosensors novel optical fiber photonics programs sensor specific biomarkers tau Proteins tomography two-dimensional waveguide

项目摘要

We propose a new research paradigm aimed at addressing scientific questions in both biosensing and machine learning for the early prediction of Alzheimer's disease (AD), and at solving a grand challenge in the identification of minimally-invasive AD biomarkers in tear, saliva, and blood. Our goal is to develop a novel and minimally-invasive system that integrates a multimodal biosensing platform and a machine learning framework, which synergistically work together to significantly enhance the detection accuracy. The program will pioneer a novel Multimodal Optical, Mechanical, Electrochemical Nano-sensor with Twodimensional material Amplification (MOMENTA) platform for sensitive and selective detection of AD biomarkers. The sensor outputs are used for training the new Hierarchical Multimodal Machine Learning (HMML) framework, which not only automatically integrates the heterogeneous data from different modalities but also ranks the importance of different biosensors and biomarkers for AD prediction. Moreover, the framework is able to identify potential new biomarkers based on a statistical analysis of the learned weights on the input signals and provide feedback information to further improve the MOMENTA platform design. This interdisciplinary research brings together materials scientists who create new twodimensional (2D) material platforms for sensor enhancement, nanotechnology and device experts who advance chip-scale sensor platforms, data scientists who analyze data with machine learning methods to target early prediction of AD, and AD experts who help to identify potentially new AD biomarkers. The machine-learning-enhanced multi-modal sensor system will not only offer major performance boost compared to state-of-the-art, but also yield critical insights on new biomarker discovery for AD diagnosis at an early stage.

我们提出了一种新的研究范式，旨在解决生物传感和生物传感领域的科学问题。机器学习用于早期预测阿尔茨海默氏病（AD），并解决一个巨大的挑战泪液、唾液和血液中微创 AD 生物标志物的鉴定。我们的目标是开发一个集成多模式生物传感平台和机器的新颖微创系统学习框架，协同工作以显着提高检测精度。该项目将开创一种新型多模态光学、机械、电化学纳米传感器，具有二维用于灵敏和选择性检测 AD 的材料放大 (MOMENTA) 平台生物标志物。传感器输出用于训练新的分层多模式机器学习（HMML）框架，不仅自动集成来自不同领域的异构数据模式，还对不同生物传感器和生物标志物对 AD 预测的重要性进行了排名。此外，该框架能够根据对生物标志物的统计分析来识别潜在的新生物标志物。学习输入信号的权重并提供反馈信息以进一步改进 MOMENTA 平台设计。这项跨学科研究汇集了材料科学家，他们创造了新的二维材料用于传感器增强、纳米技术和设备专家的 (2D) 材料平台先进的芯片级传感器平台、使用机器学习方法分析数据的数据科学家针对 AD 的早期预测，以及帮助识别潜在的新 AD 生物标志物的 AD 专家。这机器学习增强型多模态传感器系统不仅会带来显着的性能提升与最先进的技术相比，还对 AD 诊断的新生物标志物发现产生了重要见解早期阶段。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BRITISH BIOMEDICAL BULLETIN

英国生物医学通报

DOI：
发表时间：
2024-09-14
期刊：
影响因子：
0
作者：
J. Patil;P. Gurav;Ravindra Kulkarni;Sachin Jadhav;S. M;ave;ave;Meghanath B. Shete;V. Chipade
通讯作者：
V. Chipade

Recent Advances in 2D Material Theory, Synthesis, Properties, and Applications.

二维材料理论、合成、性能和应用的最新进展。

DOI：
10.1021/acsnano.2c12759
发表时间：
2023-05-23
期刊：
ACS nano
影响因子：
17.1
作者：
Yu‐Chuan Lin;R. Torsi;Rehan Younas;C. Hinkle;A. Rigosi;H. Hill;Kunyan Zhang;Shengxi Huang
通讯作者：
Shengxi Huang

pADR: Towards Personalized Adverse Drug Reaction Prediction by Modeling Multi-sourced Data.

pADR：通过对多源数据建模实现个性化药物不良反应预测。